Business dynamics and productivity

Business Dynamics
and Productivity


Business Dynamics
and Productivity


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PREFACE

Preface

A

dynamic business environment plays an important role not only as a key driver of job
creation but also as an engine of productivity growth. A growing body of research
highlights significant differences in business dynamics across countries and over time, in
particular over the different phases of the business cycle. However, our understanding of
these differences remains patchy, and this makes it more difficult for policy makers to
implement economically efficient policies.

This collection of studies aims to fill this gap by providing new evidence on business
dynamics from a cross-section of countries of different sizes, with different market and
structural characteristics, and which are at different stages in their development process.
The studies focus in particular on Belgium, Brazil, Canada, Costa Rica, Japan, New Zealand,
Norway and the United Kingdom, shedding new light on how firms which differ in terms
of their size, age, sector and other characteristics, respond to economic shocks, with a
particular focus on differences in their responses to the last decade’s global financial crisis.
Evidence collected in this volume also aims to provide a better understanding of the
contribution of business dynamics to aggregate productivity and of the effects of economic

policies across different firms and countries. Thus, it will help policy makers design better
policies, harnessing productivity and employment growth in support of more inclusive and
sustainable societies.
The work presented here is part of a broader effort by the OECD to provide evidence on
business dynamics and productivity from firm-level data, drawing on a variety of
methodologies. In particular, the OECD is leading two projects – DynEmp and MultiProd –
that use countries’ representative firm-level data to conduct comparable cross-country
analysis on employment dynamics and productivity. This study draws on the insights of
this research, providing not only cross-country comparability but also the opportunity to
dig deeper than aggregate or sectoral averages to uncover differences across firms, describe
productivity and employment distributions, and analyse heterogeneous impacts of
policies. At the leading edge of these new approaches, the OECD has a valuable role to play
in helping to strengthen the empirical analysis in support of better policies.
The pages which follow are an important step in that direction, which leverages the
expertise of the DynEmp and MultiProd network members. This collaborative and forwardlooking work will help policymakers design better policies by harnessing productivity and
employment growth in support of more inclusive and sustainable societies.

Angel Gurría
OECD Secretary-General

BUSINESS DYNAMICS AND PRODUCTIVITY © OECD 2017

3



FOREWORD

Foreword

T

his volume is part of a wider effort led by the OECD Directorate for Science, Technology and
Innovation to provide new cross-country evidence on employment dynamics and productivity based on
firm-level micro-data. In this context, the OECD is co-ordinating two distributed micro-data projects
– DynEmp and MultiProd – that rely on micro-aggregated data from a broad cross-section of countries
for comparable cross-country analyses on employment dynamics and productivity, respectively (see
www.oecd.org/sti/DynEmp.htm and www.oecd.org/sti/ind/MultiProd.htm). The innovative
methodology applied by the OECD allows for the collection and analysis of harmonised data based on
confidential administrative sources or official representative surveys. Both DynEmp and MultiProd rely
on the active participation of a network of national experts who have expertise in these different areas
and who have access to the relevant micro-data sources in their respective countries.
The projects allow for the assessment of the effects of national policies and framework conditions
on different firm-level outcomes. On the one hand, the cross-country dimension of the project overcomes
one of the great shortcomings of studies which rely on data from a single country, namely the relatively
limited variation in policy settings. On the other hand, unlike cross-country studies that concentrate on
outcomes at higher levels of aggregation, the methodology allows for the analysis of the heterogeneous
responses of different economic actors to the very same policy settings. The OECD has a particularly
important role to play in helping to bridge this gap. The distributed micro-data approach offers a
unique chance for building and exploiting longitudinal databases, and for going beyond cross-sectional
cross-country comparisons or aggregate industry-level analysis. In this framework, DynEmp and
MultiProd allow for the generation of data suitable for analysing specific economic policy questions at
different levels of aggregation (sectoral, geographical, or based on the size and age of firms).
However, DynEmp and MultiProd, by their very nature and to ensure comparability, have to
combine the availability of data in the majority of the participating countries with a shared interest in
the policy questions under investigation. For this reason, this book builds upon the great expertise of the
DynEmp network’s members in order to push further the boundaries of the DynEmp project, focusing on
three different directions: data needs, methodology, and – most importantly – policy questions.

BUSINESS DYNAMICS AND PRODUCTIVITY © OECD 2017

5


ACKNOWLEDGEMENTS

Acknowledgements

T

his volume was edited by Chiara Criscuolo from the OECD Directorate for Science,
Technology and Innovation, who also authored Chapter 1.
Danilo Coelho, Carlos Henrique Corseuil, and Miguel Nathan Foguel from the Instituto
de Pesquisa Econômica Aplicada (IPEA) wrote Chapter 2, with research assistance from
Luciana Costa and Katcha Poloponsky.
Michel Dumont, Chantal Kegels, Hilde Spinnewyn and Dirk Verwerft from the Belgian
Federal Planning Bureau are the authors of Chapter 3.
The authors of Chapter 4 are Michael Anyadike-Danes and Mark Hart from the Enterprise
Research Centre and Aston Business School.
Chapter 5 was prepared by Richard Fabling, independent researcher, and David Maré
from the Motu Economic and Public Policy Research Centre in New Zealand.
Chapter 6 was authored by Catalina Sandoval, Francisco Monge, Tayutic Mena, Arlina
Gómez and David Mora from the Ministry of Foreign Trade of Costa Rica.
Jay Dixon, from the Department of Innovation, Science and Economic Development
Canada, authored Chapter 7, which benefitted from extensive comments by Pierre Therrien
from the same Department.
Chapter 8 was authored by Arvid Raknerund and Diana-Cristina Iancu from Statistics
Norway, and benefited from comments and suggestions from Thomas von Brasch,
Chiara Criscuolo, Carl Gjersem, Erik Storm and Nora Kirsten Sundvall.
Finally, Chapter 9 was written by Kenta Ikeuchi, from the Research Institute of Economy,

Trade and Industry (RIETI) in Japan.
The book benefited from the inputs of the OECD Secretariat, with special thanks going
to Flavio Calvino for his support throughout the production of the book. Isabelle DesnoyersJames, Angela Gosmann, Fabienne Barrey and Elisaveta Gekova for provided statistical and
editorial support.
The DynEmp and MultiProd projects would have not been possible without the support
from the Committee for Industry, Innovation and Entrepreneurship (CIIE) and the Working
Party of Industry Analysis (WPIA), and the generous contributions from a network of
researchers and policy makers from around the globe. The table below lists them and their
institutions by country.

6

BUSINESS DYNAMICS AND PRODUCTIVITY © OECD 2017


ACKNOWLEDGEMENTS

Country

National representative(s)

Institution(s)

Australia

Antonio Balaguer, Diane Braskic, David Hansell

Department of Industry, Innovation and Science and Australian
Bureau of Statistics

Austria

Werner Hoelzl, Jürgen Janger, Michael Peneder

WIFO Institute (Austrian Institute of Economic Research)

Belgium

Michel Dumont, Chantal Kegels, Hilde Spinnewyn

Federal Planning Bureau

Brazil

Carlos Henrique Leite Corseuil, Gabriel Lopes de Ulyssea,
Glaucia Estafânia de Sousa Ferreira, Alexandre Messa Peixoto
da Silva, Fernanda De Negri

Instituto de Pesquisa Econômica Aplicada (IPEA)

Canada

Pierre Therrien, Jay Dixon, Anne-Marie Rollin, John Baldwin,
Wulong Gu

Industry Canada and Statistics Canada

Chile

Antonio Martner Sota, Andrés Zahler

Ministerio de Economía, Fomento y Turismo

China

Keiko Ito, Kyosuke Kurita, Yoshihiro Hashiguchi

Senshu University, Kwansei Gakuin University, OECD

Costa Rica

Alonso Alfaro, David Bullon Patton, Arlina Gómez, Tayutic Mena,
Francisco Monge

Central Bank of Costa Rica and Ministry of Foreign Trade

Denmark

Dorte Høeg Koch, Morten Skov Poulsen

Ministry for Business and Growth

Finland

Mika Maliranta

ETLA and Statistics Finland

France

DynEmp and MultiProd teams

OECD

Hungary

Adrienn Szep Szollosine, Erzsebet Eperjesi Lindnerne, Gabor Katay,
Peter Harasztosi, Mihály Szoboszlai

Central Bank of Hungary, Hungarian Central Statistical Office

Germany

Anke Rink, Natalie Rosenski

DESTATIS – Federal Statistical Office of Germany

Indonesia

Keiko Ito, Kyosuke Kurita

Senshu University, Kwansei Gakuin University

Italy

Stefano Costa

Italian National Institute of Statistics (ISTAT)

Japan

Kyoji Fukao, Kenta Ikeuchi and Keiko Ito

Hitotsubashi University, National Institute of Science and Technology
Policy and RIETI

Luxembourg

Leila Peltier – Ben Aoun, Chiara Peroni, Umut Kilinc

STATEC

Netherlands

Michael Polder

Statistics Netherlands (Centraal Bureau voor de Statistiek)

New Zealand

Corey Allan, Lynda Sanderson, Richard Fabling

Ministry of Business, Innovation and Employment, independent
researcher, Motu Economic and Public Policy Research Trust

Norway

Arvid Raknerud, Diana-Cristina Iancu

Statistics Norway and Ministry of Trade and Industry

Portugal

Jorge Portugal, Silvia Santos, Ana Gouveia, Luís Guia, Guida Nogueira, Presidencia da Republica, Min. Finanças, Min. Economia
Ricardo Alves

Spain

Valentin Llorente Garcia

Spanish Statistical Office

Sweden

Eva Hagsten, Fredrik Andersson

Statistics Sweden

Turkey

Faik Yücel Günaydın

Ministry of Science, Industry and Technology

United Kingdom

Michael Anyadike-Danes, Richard Prothero, Giovanni Mangiarotti

Aston Business School, ONS

United States

Lucia Foster, Kristin McCue, Javier Miranda, Shawn Klimek

Center for Economic Studies, US Census Bureau

OECD

Giuseppe Berlingieri, Patrick Blanchenay, Sara Calligaris, Flavio Calvino,
Alessandra Colecchia, Chiara Criscuolo, Isabelle Desnoyers-James,
Peter Gal, Nicholas Johnstone, Carlo Menon, Dirk Pilat, Mariagrazia
Squicciarini, Andrew Wyckoff

BUSINESS DYNAMICS AND PRODUCTIVITY © OECD 2017

7



TABLE OF CONTENTS

Table of contents
Executive summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
List of acronyms and abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

15
18

Chapter 1. Assessing the links between business dynamics and policy settings . . . .
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Going beyond the average firm paradigm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Organic versus non-organic growth. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

21
22
24
27

The impact of the crisis on employment stocks, flows and business dynamics . .
The role of sectors, ownership and trade status for job creation and destruction
and business dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Business dynamics, reallocation and productivity . . . . . . . . . . . . . . . . . . . . . . . . . . .

28
30
31

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

33

Chapter 2. Employment growth of establishments in the Brazilian economy:
Results by age and size groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The plant employment dynamics over their life cycle . . . . . . . . . . . . . . . . . . . . . . . .
The “missing middle” and establishment size distribution in Brazil . . . . . . . . . . . .
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

35

36
38
39
46
54

Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

55
56

Annex 2.A1. Complementary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

57

Annex 2.A2. Methodological details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

58

Chapter 3. The role of mergers and acquisitions in employment dynamics in Belgium .
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data section. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Organic growth versus growth through acquisition . . . . . . . . . . . . . . . . . . . . . . . . . .
Employment effects of M&As . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The probability of acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59
60

61
65
69
77
82

Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

83
85

Chapter 4. Firm and job dynamics in the United Kingdom before, during and after
the global financial crisis: Getting in under the hood . . . . . . . . . . . . . . . . . . . . . . .
Context, motivation and approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data sources and construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

87
88
89

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Accounting for continuing firms and their jobs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
90

The facts of firm and job dynamics, 1998-2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
93
Some counterfactual calculations for the GFC period. . . . . . . . . . . . . . . . . . . . . . . . . 102
What have we learned? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
Chapter 5. Cyclical labour market adjustment in New Zealand: The response
of firms to the global financial crisis and its implications for workers. . . . . . . . .
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Labour market resilience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Recent cyclical variation in New Zealand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Microeconomic sources of aggregate adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

111
112
113
115
122
124
137

Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
Chapter 6. Employment dynamics in Costa Rica after the global financial crisis . . . . . 143
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
A stable economy relying on open markets, still adapting to new industrial
dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
Data and methodological approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Annex 6.A1. Unemployment rate by skills level, 2010-15 . . . . . . . . . . . . . . . . . . . . . . . . . . 164
Annex 6.A2. Technical note: Estimation of long-term or structural unemployment rate . . 165
Annex 6.A3. Estimated values and statistic tests from unemployment equation . . . . . . 166
Annex 6.A4. Unemployment structure by type, 2011-15 . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Annex 6.A5. Resulting final sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
Annex 6.A6. List of variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
Annex 6.A7. Share of employment by economic activity . . . . . . . . . . . . . . . . . . . . . . . . . . 170
Chapter 7. The growth of Canadian firms: Evidence using different growth measures . .
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Models of firm growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

171
172
173
175
177
178
185

Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
Annex 7.A1. Full median VAR regressions by size and age . . . . . . . . . . . . . . . . . . . . . . . . . 188

10

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Chapter 8. Employment dynamics and labour productivity growth in the Norwegian
economy: Evidence from firm-level data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Productivity growth decompositions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

191
192
192
196

Productivity growth in the Norwegian mainland economy 1996-2014 . . . . . . . . . . . 196
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
Annex 8.A1. Proofs and supplementary figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
Chapter 9. Employment and productivity dynamics during economic crises in Japan . . .
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Economic crises in Japan over three decades . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Firm-level reallocation and crises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

211
212
213
215
222

Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
Tables
2.1. Actual minus predicted shares of the establishment size distribution
2.2.
2.3.
2.4.
2.5.

3.1.
3.2.
3.3.
3.4.
3.5.
3.6.
3.7.
3.8.
3.9.

for the whole formal sector for 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Actual minus predicted shares of the establishment size distribution:
Manufacturing sector, 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Actual minus predicted employment shares for the whole formal sector
for 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Actual minus predicted employment shares for the manufacturing sector
for 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Actual minus predicted shares of establishment size and employment
share distributions for the whole formal sector and the manufacturing
sector for 2013 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Top ten industries with the highest share of firms involved in a deal,
2001-14. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Share (% of total number of firms/employment) of target and acquiring
firms, 2001-14 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Age distribution of target and acquirer in M&A deals involving Belgian
firms in percentage, 1997-2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Net job creation (absolute values and as share of total employment)
of Belgian firms by age and M&A status, 2002-14 . . . . . . . . . . . . . . . . . . . . . . . . .
Belgian firms involved in acquisitions as a share of high growth Belgian
firms in percentage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Status of Belgian target firms, 1997-2015 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Employment growth of Belgian target firms, 2001-14 . . . . . . . . . . . . . . . . . . . . .
Employment growth of Belgian target firms (not dissolved) by type of deal,
2001-14. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Employment growth of Belgian target firms before and after acquisition
by age group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3.10. Employment growth of Belgian target firms before and after acquisition
by Pavitt category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.11. Employment growth of Belgian acquiring firms before and after acquisition,
2001-14. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.12. Employment growth of Belgian acquiring firms before and after acquisition,
3.13.
3.14.
5.1.
5.2.
5.3.
5.4.
5.5.
5.6.
5.7.
5.8.

5.9.
6.1.
6.2.
7.1.
7.2.
7.3.
7.4.
7.5.
7.6.
9.1.
9.2.
9.3.
9.4.

2001-14. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Probability of a Belgian firm being acquired, 2006-14. . . . . . . . . . . . . . . . . . . . . .
Probability of a Belgian firm acquiring another firm, 2006-14 . . . . . . . . . . . . . .
Maximum cumulative decline in output and employment. . . . . . . . . . . . . . . . .
Grouping of ANZSIC96 industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Changes in job and worker flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Decomposition of growth in employment and wages . . . . . . . . . . . . . . . . . . . . .
Changes in industry means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Modelling worker flows conditional on employment growth:
Regression results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Modelling the response to output shocks: Regression results . . . . . . . . . . . . . .
Firm and industry characteristics conditional on output shocks . . . . . . . . . . . .
Firm and industry characteristics conditional on employment change . . . . . .
Descriptive statistics of firms by size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Average age and condition of activity of firms, by size and year . . . . . . . . . . . .
Unconditional growth rates: summary statistics . . . . . . . . . . . . . . . . . . . . . . . . .

Quantile regression: results at the median, all firms . . . . . . . . . . . . . . . . . . . . . .
Select median regressions, by firm employment . . . . . . . . . . . . . . . . . . . . . . . . .
Select median regressions, by firm age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Select regressions at the 10th and 90th quantile, by firm employment . . . . . .
Select regressions at the 10th and 90th quantile, by firm age. . . . . . . . . . . . . . .
Number of firms: manufacturing industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Number of firms: non-manufacturing industries . . . . . . . . . . . . . . . . . . . . . . . . .
Reallocation effects and economic crisis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Reallocation effects and economic crisis by sector. . . . . . . . . . . . . . . . . . . . . . . .

74
76
76
79
80
117
121
125
128
132
132
133
134
135
152
152
178
179
181
182

183
184
217
218
220
221

Figures
1.1.
1.2.
1.3.
1.4.
1.5.
2.1.
2.2.

Young firms have higher employment growth rates . . . . . . . . . . . . . . . . . . . . . .
Average employment level by firm age. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Across countries the average start-up employs less than ten employees. . . . .
Most micro start-ups remain micro-firms five years after entry . . . . . . . . . . . .
Job creation, job destruction and churning rate . . . . . . . . . . . . . . . . . . . . . . . . . .
Average employment level by age of establishment. . . . . . . . . . . . . . . . . . . . . . .
Average employment level by age of establishment and size
of establishment at birth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3. Age effect on plant size using decomposition method . . . . . . . . . . . . . . . . . . . .
2.4. Year and cohort effects on plant size using the decomposition method . . . . .
2.5. Age effect on plant size according to decomposition method: Results
by establishment birth size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.6. Average employment level by age of establishment: Surviving and closing
plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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26
26
27
29
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40
41
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2.7. Average employment level at birth and death by age of establishment
at death . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.8. Mortality rate by age of establishment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.9. Age effect in the relative number of plants according to decomposition
method: Results by establishment birth size. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.10.
2.11.
2.12.
2.13.
3.1.

3.2.
3.3.
3.4.
3.5.
3.6.
4.1.
4.2.
4.3.
4.4.
4.5.
4.6.
4.7.
4.8.
4.9.
4.10.
4.11.
4.12.
5.1.
5.2.
5.3.
5.4.
5.5.
5.6.
5.7.
6.1.
6.2.
6.3.
6.4.
6.5.

Average employment growth rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unimodality and missing middle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Distribution of establishment size by number of employees, 2013 . . . . . . . . . .
Employment share by establishment size, 2013 . . . . . . . . . . . . . . . . . . . . . . . . . .
Number and value of worldwide M&As and Standard and Poor’s index . . . . . .
Number of completed acquisitions involving Belgian firms, European firms
and worldwide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Net job creation by firm age group in 18 countries, 2001-11 . . . . . . . . . . . . . . . .
Net job creation by age group of Belgian firms not involved in M&As
by percentage, 2002-13 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Log odds for Belgian firms acquiring foreign firms by earnings before
interest and tax (EBIT) rate and debt rate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Log odds of acquisition by level of market concentration (HH index) . . . . . . . .
Births: firms (thousand) and jobs per firm (jperf) . . . . . . . . . . . . . . . . . . . . . . . . .
Births: firms and jobs (thousand) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Deaths: firms (thousand) and jobs per firm (jperf) . . . . . . . . . . . . . . . . . . . . . . . .
Deaths: firms and jobs (thousand) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Death ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Contributions to change in continuing firm numbers . . . . . . . . . . . . . . . . . . . . .
Continuing firms (million) and jobs per firm (jperf) . . . . . . . . . . . . . . . . . . . . . . .
Continuing firms and jobs (million) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Contributions to change in continuing firm jobs . . . . . . . . . . . . . . . . . . . . . . . . .
Contributions of births and continuing firms to net job creation . . . . . . . . . . .
Exogenous variables for counterfactual . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Counterfactual results, jobs and firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
New Zealand output and employment cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Labour adjustment around the 2008 recession . . . . . . . . . . . . . . . . . . . . . . . . . . .
Job and worker flows by industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Variable definition: data timing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Heterogeneous adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Post-peak changes conditional on output shock. . . . . . . . . . . . . . . . . . . . . . . . . .
Post-peak changes conditional on employment growth . . . . . . . . . . . . . . . . . . .
Structure of exports of goods and services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Unemployment rate, estimated trends and GDP growth . . . . . . . . . . . . . . . . . . .
Costa Rica, employment structure by industry . . . . . . . . . . . . . . . . . . . . . . . . . . .
Share of employment by economic sector and exporting condition
of businesses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Employment growth rate by exporting and non-exporting group,

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45
48
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50
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63
66
66
81
82
94
95
96
97
97
98
99
100

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101
104
105
116
118
120
124
126
129
130
147
148
149
153

according to economic sector, 2010-12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
6.6. Relative creation, destruction and net variation of employment
in businesses according to economic sector and exporting condition . . . . . . . 156

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6.7. Total employment according to firm age, by economic sector
and exporting condition, 2012 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
6.8. Share of agriculture businesses by employment growth, size

and exporting condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
6.9. Share of manufacturing businesses by employment growth, size
and exporting condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.10. Share of services businesses by employment growth, size and exporting
condition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.11. Share of businesses by employment growth rate, size, exporting condition
and economic activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.1. Annual productivity growth in mainland economy decomposed
into contributions by industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.2. Decomposition of productivity growth into five sources: withinand between-industry reallocation; entry- and exit-effects;
and non-reallocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.3. The contribution from non-reallocation to productivity growth, by industry. . .
8.4. The contribution from between-firm reallocation to productivity growth,
by industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.5. The contribution from entry/exit dynamics to productivity growth,
by industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.6. Decomposition of employment flows, by industry . . . . . . . . . . . . . . . . . . . . . . . .
9.1. Economic crises in Japan (market economy; index: 1991 = 1) . . . . . . . . . . . . . . .
9.2. Crises and job loss by job status (index: first year of each crisis = 1) . . . . . . . . .
9.3. Crises and decomposition of the gross-output growth rate to final demand
factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.4. Number of listed companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.5. Share of sample firms in total economy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9.6. Employment growth rate by TFP class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

159
160
161
197

199
200
201
202
202
214
215
215
216
216
219

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Business Dynamics and Productivity
© OECD 2017

Executive summary

B

usiness dynamics plays an important role not only as a driver of job creation but also as
an engine of reallocation and productivity growth. This book aims at providing new evidence
on business dynamics from different countries and at shedding new light on the
heterogeneous responses of firms to economic shocks, with a particular focus on the impact
of the last decade’s global financial crisis. All chapters in this book highlight the importance
of going beyond the average firm paradigm when analysing business dynamics, accounting
for different firm characteristics such as size, age, ownership and trade status.
The analysis of the Brazilian business sector, in Chapter 2, focuses on the statistical
properties of establishment-level employment dynamics and highlights that younger
businesses are characterised by both disproportionally high employment growth rates and
high exit rates. This corroborates empirical evidence found for other countries that
participate in a cross-country distributed microdata project on business dynamics
co-ordinated by the OECD: the DynEmp project. Furthermore, the findings show that small
establishments in Brazil are born very small and are not able to reach the scale of mid-sized
establishments, even though they may grow fast at the beginning of their activity, and they
tend to die early. Relatedly, the authors also show the existence of a “missing middle” in the
Brazilian formal sector, i.e., the middle part of the business size distribution is thinner. The
magnitude of this phenomenon appears larger in Brazil than in other Latin American or
Asian countries for which comparison in the manufacturing sector is available. For policy
makers it is important to understand the role of a country’s framework conditions and the
effectiveness of targeted support policies can explain these patterns. For these reasons, in
depth policy evaluations within countries and cross-country analysis in the lines of the
OECD DynEmp project are key for providing the relevant evidence base.
To fully understand the dynamics of business creation and growth, one needs to know
whether businesses grow through the creation of new jobs within the firm (organic growth)
or whether their expansion relies on mergers or acquisitions (non-organic growth).
Chapter 3 focuses on this issue and quantifies the role of mergers and acquisitions for
employment dynamics in the country. Findings suggest that domestic acquisitions and
intra-industry acquisitions have negative effects on employment in target firms, which are
partially offset by job creation in Belgian acquiring firms. However, in the case of interindustry acquisitions, the acquisition of a Belgian target by a foreign firm appears to have a

positive impact on employment in the Belgian target. Thus, acquisitions seem instrumental
in achieving high growth, but young Belgian firms appear to be less inclined to acquire other
firms than young foreign firms to acquire domestic firms.
The DynEmp project has documented a change in the patterns of business dynamics
over the business cycle. The sharp increase in gross job destruction and the drop in gross
job creation that occurred in the 2008-09 biennium contracted only partially over the 2009-10

15


EXECUTIVE SUMMARY

biennium, with creation and destruction rates eventually aligning to pre-crisis levels during
the following two-year period. Forthcoming OECD evidence demonstrates that only a few
countries have shown a strong resilience to the global financial crisis, suggesting that the
crisis’ effects on start-ups are probably long-lasting.
Results for the United Kingdom concentrate on the dynamics of the stocks of firms and
jobs, using an intuitive but very effective decomposition framework that allows separately
analysing births, deaths and “continuing” firms. The findings highlight the importance of the
variation in firm births, especially over the global financial crisis, and the relatively limited
variation in average jobs per firm. The distinctive feature of the global financial crisis in the
United Kingdom has been a collapse in business entries, which negatively affected the stock
of continuing firms in the following year, leading to job destruction. Results suggest an
important policy message: “today’s start-ups are tomorrow’s continuing firms”.
New Zealand’s results focus on the dynamics of employment adjustments during the
2000s, concentrating in particular on the aftermath of the global financial crisis in the
country. Findings highlight the existence of significant heterogeneity across firms, both
before and after the crisis, not only in the size of output shocks but also in the amount of
employment adjustments and in the size of worker flows resulting from such adjustments.
The analysis links the labour market policy settings in a given country and the level of that

country’s resilience to cyclical shocks of different intensity, with a particular focus on the
effects of the financial crisis on worker flows and firm exit rates. Results confirm that worker
flows significantly lowered during the crisis in New Zealand, but do not find support for
higher exit rates. The chapter provides evidence that a labour market that is resilient to
cyclical shocks – thanks to smoothing policies – may not be suited to respond to shocks that
require significant reallocation of employment, which would need a policy mix facilitating
retraining, job turnover and reallocation, as well as geographic and industry mobility.
Costa Rica’s evidence focuses on the labour market adjustments in the country in the
aftermath of the global financial crisis, where increased unemployment seems to reflect
structural change and a mismatch between labour supply and demand. Despite Costa Rica
being still characterised by high shares of employment in manufacturing and agriculture, the
country is increasingly transforming into a service-oriented economy. The analysis
highlights that business dynamics for firms of different ages and sizes in Costa Rica depends
on their sector of activity and on whether they are engaged in international trade. Exporting
micro, small and medium-sized enterprises (MSME) are growing faster than those focusing
on the domestic market; and those operating within the Free Trade Zone regime are more
likely to growth than firms outside the regime. Most exporting MSMEs in services increased
employment, while this share has been more limited in agriculture and manufacturing.
Results for Canada concentrate on the complex process of firm growth, analysing
simultaneously how different dimensions of such a process – namely employment, sales,
profits and productivity – interact. Exploiting a unique database that allows focusing on
organic growth, the results highlight that the median firm grows very little according to all
outcome variables considered. The findings also suggest that sales growth appears to drive
subsequent growth in all other variables. Profits present a similar pattern but at a lower level.
When one focuses on different firm size and age classes, profits seem to have a comparatively
greater effect on growth for smaller and younger firms, but such effect remains small.
By applying a novel decomposition methodology, results for Norway quantify the
contributions to employment and labour productivity growth from different sectors and

16

BUSINESS DYNAMICS AND PRODUCTIVITY © OECD 2017


EXECUTIVE SUMMARY

different firms. The authors find that the decline in productivity growth observed at the
beginning of the 2000s is related to the strong employment growth via entry in some services
sectors with low initial productivity levels. An explanation for the productivity slowdown in
the country may be sought in the growing role of wholesale and retail trade and information
and communication, combined with the decline of manufacturing. Large continuing firms
are the main contributors to labour productivity growth, but they contribute little or
negatively to employment growth, with new jobs being created mainly by entrants and SMEs.
Exporters contribute similarly to aggregate productivity growth and employment growth,
whereas non-exporting firms tend to contribute gradually less to productivity growth but
remain the largest contributors in terms of employment growth.
Finally, the evidence for Japan focuses on the cleansing effects of economic crises during
the last 20 years of the country’s economic cycle. By considering four crisis periods, the
findings show that during these crises labour inputs and productivity decreased sharply,
while in the recovery periods following the crises labour inputs did not increase despite
productivity increases. The results further focus on the effect of the crises on within-industry
reallocation and show that the labour inputs reallocation has been productivity-enhancing in
Japan. Only in the case of the global financial crisis the productivity-enhancing reallocation
mechanism was not strengthened by the downturn. Interestingly, the universal nature of the
global financial crisis is considered a possible explanation of this result for Japan.

BUSINESS DYNAMICS AND PRODUCTIVITY © OECD 2017

17

LIST OF ACRONYMS AND ABBREVIATIONS

List of acronyms and abbreviations
ALU
AUC
BCCR
BOS
BSD
CES
COMEX
DBJ
EBIT
ECLAC
EFTA
ERA
EU
FDI
FTA
FTZ
GATT
GDP
GFC
GST
HH
HLFS
IMAA
ISIC
IT
ITA

JCR
JDR
JIP
LBD
LCI
LEED
LR
M&A
MSME
NA
NACE

18

Average labour unit
Area under the curve
Central Bank of Costa Rica
Business Operations Survey
Business Structure Database
Constant elasticity of substitution
Ministry of Foreign Trade of Costa Rica
Development Bank of Japan
Earnings before interest and tax
Economic Commission for Latin America and the Caribbean
European Free Trade Association
Employment Relations Act
European Union
Foreign direct investment
Free trade agreement
Free trade zone

General Agreement on Tariffs and Trade
Gross domestic product
Global financial crisis
Goods and services tax
Herfindahl-Hirschman
Household Labour Force Survey
Institute for Mergers, Acquisitions and Alliances
International System of Industrial Classification
Information technology
Information Technology Agreement
Job creation rate
Job destruction rate
Japan Industrial Productivity
Longitudinal Business Database
Labour Cost Index
Linked employer-employee data
Likelihood ratio
Merger and acquisition
Micro, small and medium-sized enterprise
National Accounts
Nomenclature générale des activités économiques dans les communautés
européennes

BUSINESS DYNAMICS AND PRODUCTIVITY © OECD 2017


LIST OF ACRONYMS AND ABBREVIATIONS

NEG
OLS

PAYE
QES
R&D

Net employment growth
Ordinary least squares
Pay as you earn
Quarterly Employment Survey
Research and development

ROC
S&P
SME
TFP
TiVA
UK
US
VAT
VCE
WTO

Receiver operating characteristic
Standard and Poor
Small and medium-sized enterprise
Total factor productivity
Trade in Value-Added
United Kingdom
United States
Value-added tax
Variance-covariance matrix of the estimator

World Trade Organization

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19



Business Dynamics and Productivity
© OECD 2017

Chapter 1

Assessing the links between business
dynamics and policy settings

This chapter presents an overview of the chapters included in this volume and relates
them with the cross-country evidence gathered by the OECD Dynemp project,
highlighting common trends and differences. Based on analyses of data from Belgium,
Brazil, Canada, Costa Rica, Japan, the United Kingdom, Norway and New Zealand, the
studies illustrate how firm characteristics (age, size, sector) and economic conditions
(market conditions, stage in the business cycle and in economic development) affect
employment growth, firm performance, resource allocation and productivity growth.
The results shed new light on the important role played by the recent global financial
crisis on OECD countries and emerging economies.

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ASSESSING THE LINKS BETWEEN BUSINESS DYNAMICS AND POLICY SETTINGS

Introduction
Business dynamics is an important driver of job creation and productivity growth. A
growing body of evidence shows large differences in business dynamics across countries
and over time, in particular over the business cycle. However, our understanding of these
differences remains limited, adversely affecting efficient policy design. The purpose of this
book is to fill this gap by providing new evidence from countries at different stages of
development and of different sizes. It also tries to provide evidence of firms’ heterogeneous
responses to shocks and to investigate the impact of one of the biggest shocks that firms
have had to face in the last decade: the global financial crisis. The hope is that the evidence
collected in this volume will help readers to better understand business dynamics and the
heterogeneous impact of policies and framework conditions across different firms and
countries and help policy makers to design better policies that can foster both employment
and productivity growth.
The book is part of a larger effort by the OECD Directorate for Science, Technology and
Innovation to provide new evidence on employment dynamics and productivity across
countries exploiting firm-level data. As part of the same strand of work, the OECD is leading
two projects, DynEmp and MultiProd, that have relied on countries’ confidential micro-data
to conduct comparable cross-country analysis on employment dynamics and productivity,
respectively (see also www.oecd.org/sti/DynEmp.htm and www.oecd.org/sti/ind/MultiProd.htm).
The two OECD projects collect and analyse harmonised cross-country micro-aggregated data
from administrative data or official representative surveys, such as business registers, social
security and corporate tax records or national statistics offices’ surveys of production. Both
projects rely on the active participation of a network of national experts who have access to
the relevant micro-data sources in the respective countries.
The value of such exercises rests on the ability to assess the effects of different policy
settings on firm-level outcomes. On the one hand, country-specific studies are often
constrained by the relatively limited variance of policy settings (except in limited cases such

as in federal systems). On the other hand cross-country studies which focus on outcomes at
higher levels of aggregation cannot capture the heterogeneity of responses of different actors
to the same policy settings. The OECD has a particularly important role to play in helping to
bridge this gap. This approach offers a unique opportunity for creating longitudinal data that
go beyond cross-sectional cross-country comparisons or industry-level statistics. The
projects can generate data designed to answer specific policy questions and at different
sectoral or geographical levels. The program codes are modular and can easily be modified to
allow for additional categorisation of the micro-data according to firm characteristics not
considered in previous analysis (e.g. for ownership or trade status).
In fact, while considerable progress has been made in recent years in providing
researchers with secure access to official micro-data on firms at country level, significant
obstacles remain in terms of transnational access. The challenges of transnational access are
many, starting from locating and documenting information on available sources and their

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ASSESSING THE LINKS BETWEEN BUSINESS DYNAMICS AND POLICY SETTINGS

content (i.e. coverage, variables, classifications, etc.) and on accreditation procedures
(i.e. eligibility, rules, costs and timing). There are language barriers, as translated versions of
information on data and accreditation procedures seldom exist or are incomplete. In
addition, completing country-specific application forms for accreditation procedures is often
demanding and different procedures exist for data held by different agencies even within the
same country. Finally, data access systems differ across countries, implying that while
remote access or execution could be possible in some countries, in others it is only possible

to access on site, requiring researchers to travel to the location in question. These are just
some of the challenges related to accessing data, before researchers can even begin
confronting differences in the content and structure of micro-data themselves, and the time
and human capital investment required to become acquainted with the “nitty gritty” of each
database.
As a result, multi-country studies requiring the exploitation of micro-data are very
difficult to conduct, and often rely on the formation and co-ordination of networks of
national researchers, with each team having access to their respective national micro-data.
The comparability of the country level results needs therefore to be insured via the use of
a common protocol for data collection and aggregation and a common model specification
for the econometric analysis.
This methodology followed in the DynEmp and MultiProd project is called distributed
micro-data analysis, which involves writing a computer code by OECD and then running this
code in a decentralised manner by representatives in national statistical agencies or experts
in public institutions who have access to the national micro-level data, who have access to
the national micro-level data. At this stage, micro-aggregated data is generated by the
centrally designed, but locally executed, program codes, which are then sent back for
comparative cross-country analysis to the OECD. These data reduce confidentiality concerns
as they aggregate information at a sufficiently high level, and achieve a high degree of
harmonisation as the definition of the extracted information is the same, ensured by the
centrally written computer routine.
Despite a few instances when a similar approach has been used in the past – in
academic circles as well as within the OECD, the World Bank and more recently the European
Central Bank – this procedure is still not widely applied today when collecting statistical
information. This may have to do with the amount of time needed to set up and manage the
network as well as developing a well-functioning, “error-free” program code which is able to
both accommodate potential differences across national micro-level databases and
minimise the burden on those who have access to the data and run the code.
The DynEmp project is based on a distributed data collection exercise aimed at creating
a harmonised cross-country micro-aggregated database on employment dynamics from

confidential micro-level sources. The primary sources of firm and establishment data are
national business registers and for some countries, such as Brazil, social security records.
The first phase of the project was implemented in the first half of 2013 and was called
“DynEmp Express”. This first phase was based on a simplified statistical routine which led to
the collection of a database covering 18 countries. The second phase of the project, called
DynEmp v.2, aims at building a database which contains more detailed data on the withinsector contribution of start-ups and young firms to employment growth, with the aim of
analysing the role played by national policies and framework conditions for employment
growth (see e.g. Calvino, Criscuolo, and Menon 2015). At the time of writing, 22 countries

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have been successfully included in the DynEmp v.2 database (Australia, Austria, Belgium,
Brazil, Canada, Costa Rica, Denmark, Finland, France, Hungary, Italy, Japan, Luxembourg, the
Netherlands, Norway, New Zealand, Portugal, Spain, Sweden, Turkey, the United Kingdom
and the United States).
The advantages of using harmonised micro-aggregated data from business registers for
the study of business dynamics and from official surveys for productivity analysis are
manifold. First of all, for the study of business dynamics the cross-country use of business
registers allows separate identification of the different channels of employment growth,
distinguishing between gross job creation and gross job destruction, and between the
extensive (firm entry and exit) and the intensive (post-entry growth) margins. Furthermore,
the role of firm age and size can be examined separately and jointly. Finally, each of these
elements can be compared across countries, sectors and over time. Similarly, when

analysing productivity, being able to use official survey data that cover an (often stratified)
random sample of firms over time and can be reweighted using business registers in order to
be made representative allows for the reliable and comparable analysis of productivity
distributions; the description of trends in productivity dispersion over time, etc. and the
estimation of entry and exit contribution to growth, and so on.
However, the DynEmp and MultiProd projects, by their very nature and in order to
ensure comparability, rely on an approach based on a minimum common denominator
where both the policy questions are of interest and the data needed to answer those
questions are available in the large majority of participating countries. For this reason, this
volume leverages on the great expertise of the network members for the chapters in the
book to push the boundaries that contain the DynEmp project in three directions: policy
questions; methodology and data needs. The rest of this introduction will therefore
provide an overview of the different contributions, highlighting where they confirm
evidence found in the cross-country analysis and where they are novel in terms of
methodology and/or findings. This introduction will, hopefully, provide a good account of
how each of the chapters of the book contributes to building a solid evidence base for policy
making.

Going beyond the average firm paradigm
The importance of the process of creative destruction and of post-entry growth and the
ability to document these processes are pointing to the paramount importance of going
beyond the average firm paradigm and embracing firm heterogeneity in the analysis of
business dynamics. All chapters in the book are examples of how looking at firms
characteristics, such as size, age, ownership and trade status are important in shedding
further light on the process of job creation and destruction in the economy.
Chapter 2, by Danilo Coelho, Carlos Henrique Corseuil and Miguel Nathan Foguel from
the Instituto de Pesquisa Econômica Aplicada (IPEA), analyses the statistical patterns of
employment dynamics across establishments with different characteristics (e.g. size) and
at different points in their life cycle.
The chapter studies employment dynamics in the Brazilian formal sector using Relação

Anual de Informações Sociais (RAIS) data, which is a survey of all formal establishments in
Brazil collected by the labour ministry, Ministério do Trabalho e Emprego (MTE) containing
information on wages, workers and employers’ characteristics, from 1995 to 2013. The
analysis provides new empirical evidence on two main issues. Firstly, the authors examine in

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